update model card README.md

README.md

@@ -21,7 +21,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.81
+      value: 0.905
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -29,10 +29,10 @@ should probably proofread and complete it, then remove this comment. -->
 
 # distilhubert-finetuned-gtzan
 
-This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
+This model is a fine-tuned version of [NemesisAlm/distilhubert-finetuned-gtzan](https://huggingface.co/NemesisAlm/distilhubert-finetuned-gtzan) on the GTZAN dataset.
 It achieves the following results on the evaluation set:
-- Loss: 1.2001
-- Accuracy: 0.81
+- Loss: 0.7322
+- Accuracy: 0.905
 
 ## Model description
 
@@ -58,17 +58,22 @@ The following hyperparameters were used during training:
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - lr_scheduler_warmup_ratio: 0.1
-- num_epochs: 5
+- num_epochs: 10
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 0.0132        | 1.0   | 113  | 1.0964          | 0.84     |
-| 0.0003        | 2.0   | 226  | 1.2606          | 0.8      |
-| 0.1967        | 3.0   | 339  | 1.2266          | 0.8      |
-| 0.0002        | 4.0   | 452  | 1.2578          | 0.81     |
-| 0.0001        | 5.0   | 565  | 1.2001          | 0.81     |
+| 0.0002        | 1.0   | 100  | 0.5783          | 0.915    |
+| 0.1984        | 2.0   | 200  | 0.7051          | 0.91     |
+| 0.0518        | 3.0   | 300  | 1.0287          | 0.865    |
+| 0.0039        | 4.0   | 400  | 0.7660          | 0.895    |
+| 0.0001        | 5.0   | 500  | 0.7513          | 0.91     |
+| 0.0001        | 6.0   | 600  | 0.7757          | 0.9      |
+| 0.0002        | 7.0   | 700  | 0.9340          | 0.87     |
+| 0.0001        | 8.0   | 800  | 0.7237          | 0.9      |
+| 0.0001        | 9.0   | 900  | 0.7298          | 0.905    |
+| 0.0001        | 10.0  | 1000 | 0.7322          | 0.905    |
 
 
 ### Framework versions